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<TITLE>Cognitive Computer Science Research Group</TITLE>
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<h1>Cognitive Computer Science <BR> Research Group</h1>
Computer Science &Engr. Dept. (0114)<BR>
University of California - San Diego <BR>
La Jolla, CA 92093<BR>
April, 1995<BR>
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<!WA0><a HREF="http://www.cs.wisc.edu/~belew/research-stmnt.html">Richard K. Belew</a>, Director<BR>
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We are a research group within the AI laboratory.  Our name, CCSRG,
is meant to indicate that while our approach is primarily <em>computational</em>, we are  informed by a <em>cognitive</em>
perspective on both artificial and natural systems.
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The focus of our research group is the characterization of adaptive
knowledge representations.  Issues of representation have always
played a central role in artificial intelligence (AI), as well as in
computer science and theories of mind more generally. We would argue
that most of this work has (implicitly or explicitly) assumed that the
representational language is wielded <em>manually</em>, by humans
encoding an explicit characterization of what they believe to be true
of the world.  We believe there are fundamental philosophical
difficulties inherent in any such approach aside.  Further, there now
exist modern machine learning techniques capable of <em>automatically</em> developing elaborate representations of the world.  To
date, however, the representations underlying this learning have not
shown themselves able to &quot;scale up&quot; to the semantically sophisticated
task domains often associated with AI expert systems.  We believe it
is therefore appropriate to reconsider basic notions of what makes for
good knowledge representation, with constraints imposed by the
learning process considered <em>sine qua non</em> but in conjuction with
others (expressive adequacy, valid inference, etc.)  more typically
considered by AI.
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We have found it productive to pursue this general interest 
through several more specific research projects.  The first 
applies statistical techniques to  the problem  of free-text 
information retrieval (IR) and linguistics more generally.   
Many of our projects use a connectionist (neural network) 
representation of documents and descriptive keywords 
that uses relevance feedback  as a training signal to a 
reinforcement learning algorithm.  This construction allows 
an IR system to learn a more effective indexing 
representation of free-text documents as a simple by-
product of the browsing behaviors of its users.  Second, we 
have investigated a wide range of Genetic Algorithm (GAs) 
applications, ranging from use in &quot;artificial life&quot; models of 
natural phenomena to use as an artificial inductive method 
to accomplish an engineering goal like optimizing a 
function.     We believe our work in these two areas allows a 
``stereoscopic'' view of cognitive adaptation, 
encompassing a broad range of fundamental issues from  
low-level, biological constraints to high-level, symbolic 
communication.
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<H2>Current Students</H2>
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<LI>
	<!WA1><A HREF="http://www-cse.ucsd.edu/users/schraudo/">
	Nici Schraudolph</A>
<LI>
	<!WA2><A HREF="http://www-cse.ucsd.edu/users/tkammeye/">
	Tom Kammeyer</A>
<LI>
	<!WA3><A HREF="http://www-cse.ucsd.edu/users/mland/">
	Mark Land</A>
<LI>
	<!WA4><A HREF="http://www-cse.ucsd.edu/users/fil">
	Filippo Menczer</A>
<LI>
	<!WA5><A HREF="http://www-cse.ucsd.edu/users/crosin/">
	Chris Rosin</A>
<LI>
	<!WA6><A HREF="http://www-cse.ucsd.edu/users/jhatton/">
	John Hatton</A>
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<H2>Distinguished Alumnae</H2>
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<li>	Dan Rose <BR>
	Advanced Technology Group <BR>
	Apple Computer <BR>
	rose@apple.com
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<li>	John McInerney
	Advanced Technology Group<BR>
	Encyclopedia Britannica <BR>
	john@eb.com
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<LI>
	<!WA7><A HREF="http://www.cs.sandia.gov/~wehart/main.html">
	Bill Hart</A>

<LI>
	<!WA8><A HREF="http://www-cse.ucsd.edu/users/steier/">
	Amy Steier</A>

<LI>
	<!WA9><A HREF="http://www-cse.ucsd.edu/users/wwilluhn/">
	Wolfram Willuhn</A>

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<!WA10><A HREF="http://www.cs.wisc.edu/~belew/ccsrg.gif2"> If you want to see a picture... </A>

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<P><ADDRESS>
<i>rik@cs.ucsd.edu </i>
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